AI Job Titles: Entry-Level to Senior, With Descriptions

The most common AI job titles are Machine Learning Engineer, Data Scientist, AI Engineer, Research Scientist, MLOps Engineer and AI Product Manager, with specialist titles such as NLP Engineer, Computer Vision Engineer and Prompt Engineer alongside them. Below, the titles are grouped by level (entry, mid and senior), each with a one- or two-line description of the work, followed by a typical reporting line and the skills the roles share.

Artificial intelligence teams build, train, test and run models that make predictions or generate content. Some titles sit close to research, some close to software engineering, and some close to the product and the business. The same work can carry different titles at different employers, so read the description, not only the title, when you compare jobs.

AI Job Titles: Entry-Level Titles

• AI / Machine Learning Intern: a student or recent graduate who helps clean data, run experiments and write evaluation scripts under a senior engineer.
• Junior Data Scientist: analyses data, builds simple models and reports results; often the first step for people coming from statistics or analytics.
• Junior Machine Learning Engineer: writes and tests the code that trains and serves models, usually on an existing pipeline.
• Data Annotator / AI Data Specialist: labels text, images or audio so models can learn from them, and checks label quality.
• AI Trainer / Model Evaluator: writes example answers, rates model outputs and flags errors; a common entry route for writers and subject experts.
• Data Analyst (AI team): builds dashboards and pulls the data the modelling team needs.
• AI Support or Operations Analyst: monitors AI tools in production, triages issues and handles user feedback.

Mid-Level AI Job Titles

• Machine Learning Engineer: designs, trains and deploys models and keeps them reliable in production. This is the most common hands-on AI title.
• AI Engineer / Applied AI Engineer: builds products on top of existing models, for example chat assistants, search or document tools, connecting them to company data and software.
• Data Scientist: frames business questions, runs experiments and builds predictive models; usually more analysis and statistics than engineering.
• NLP Engineer: specialises in language tasks such as classification, translation, summarisation and chat.
• Computer Vision Engineer: works on image and video models for detection, recognition and inspection.
• MLOps Engineer: builds the infrastructure for training, versioning, deploying and monitoring models, close to DevOps.
• Data Engineer (ML): builds the pipelines that move and prepare the large datasets models depend on.
• Prompt Engineer: designs and tests the instructions and examples given to generative models, and builds evaluation sets to measure output quality.
• Research Engineer: turns research ideas into working experiments and code, bridging scientists and engineers.
• AI Product Manager: decides what an AI feature should do, defines how success is measured and coordinates engineering, design and legal review.
• Responsible AI / AI Ethics Analyst: reviews models for bias, safety and privacy risks and helps write the policies teams follow.

Senior and Leadership AI Job Titles

• Senior Machine Learning Engineer: owns the design of major model systems and mentors junior engineers.
• Staff or Principal ML Engineer: sets technical direction across several teams; still a hands-on, individual-contributor track.
• Research Scientist / Senior Research Scientist: develops new methods and models, runs long experiments and often publishes papers. Many of these roles ask for a PhD or equivalent research experience.
• Principal AI Scientist: leads a research area and decides which problems the group pursues.
• Machine Learning Engineering Manager: manages a team of ML engineers, hiring, planning and delivery.
• Head of AI / Head of Data Science: runs the AI function in a smaller company or a business unit.
• Director or VP of AI / Machine Learning: sets strategy and budget for several AI teams.
• Chief AI Officer: an executive title, still uncommon, responsible for AI strategy, governance and risk across the whole organisation.

Typical AI Team Hierarchy and Reporting Line

Most AI teams have two ladders that meet at the top:

1. Individual contributor track: Intern, then Junior ML Engineer or Junior Data Scientist, then ML Engineer or Data Scientist, then Senior, then Staff, then Principal.
2. Management track: Senior Engineer, then Engineering Manager, then Senior Manager or Head of AI, then Director, then VP of AI, then Chief AI Officer or Chief Technology Officer.
3. Research track: Research Engineer or Research Scientist, then Senior Research Scientist, then Principal Scientist, then Research Director.

In a small company the whole AI team may report to the CTO. In a large one, research and applied teams often sit apart, with the applied team inside a product group. You can compare these levels with the wider data job titles and the data science job titles and descriptions, which share many steps.

Skills These AI Roles Share

• Programming: Python is standard, plus SQL; engineering roles add cloud platforms and containers.
• Maths and statistics: linear algebra, probability and evaluation methods, deeper for research titles.
• Machine learning frameworks: libraries such as PyTorch, TensorFlow and scikit-learn.
• Data handling: cleaning, labelling and checking data quality, which takes much of the working week.
• Evaluation: designing tests that show whether a model is actually better, not just different.
• Communication: explaining model limits and risks to people who are not engineers.

If you are moving in from general software work, the software engineering job titles map closely onto the ML engineering ladder.

Frequently Asked Questions

• What is the most common AI job title? Machine Learning Engineer, followed by Data Scientist and, more recently, AI Engineer.
• What is the difference between an AI Engineer and a Machine Learning Engineer? An ML Engineer usually trains and deploys models; an AI Engineer more often builds applications on top of models that already exist. Many employers use the titles interchangeably.
• Which AI jobs do not require coding? AI Trainer, Model Evaluator, Data Annotator, AI Product Manager and some Responsible AI roles need little or no programming, though basic technical literacy helps.
• Is Prompt Engineer a real job title? Yes, it appears in job ads, but the work is often folded into AI Engineer or Product roles rather than kept as a separate position.
• Do AI jobs need a degree? Research Scientist roles usually ask for a graduate degree. Engineering and applied roles often accept a computer science or related degree, or equivalent experience shown through projects.

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